Back to Results
First PageMeta Content
Non-parametric statistics / Support vector machine / Normal distribution / Reproducing kernel Hilbert space / Kernel / Positive-definite kernel / Fourier analysis / Least squares support vector machine / Kernel density estimation / Statistics / Hilbert space / Statistical classification


Learning from Distributions via Support Measure Machines Krikamol Muandet
Add to Reading List

Open Document

File Size: 198,05 KB

Share Result on Facebook

City

Washington / DC / Calcutta / /

Company

kernel Kg / Neural Information Processing Systems / MIT Press / Intel / Semigroup / /

Country

United States / /

Currency

pence / /

/

Facility

Kenji Fukumizu The Institute of Statistical Mathematics / /

IndustryTerm

real-world applications / baseline algorithm / generalized inner product / inner product / nonlinear algorithm / possible applications / multimedia applications / learning algorithm / learning algorithms / /

Organization

MIT / U.S. Securities and Exchange Commission / Institute of Statistical Mathematics / /

Person

Francesco Dinuzzo / A. Berlinet / A. Vedaldi / V / Thomas C. Agnan / Bernhard Sch¨olkopf / Philipp Hennig / Noah A. Smith / Nat / Arthur Gretton / Zoubin Gharamani / Pedro M. Q. Aguiar / Eric P. Xing / Christian Walder / Kenji Fukumizu / /

Position

Prime Minister / bayesian hierarchical model for learning natural scene categories / /

ProgrammingLanguage

R / DC / /

PublishedMedium

Journal of Machine Learning Research / /

Technology

learning algorithm / baseline algorithm / DNA Chip / gene expression / Machine Learning / SMM algorithms / nonlinear algorithm / /

SocialTag